We propose a vehicle counting solution from UAV imagery based on detections, i.e., first, we localize the cars, and then we count the found instances. We relied on YOLOv3 (You Only Look Once) [1]}, a popular deep fcn, employed in many detection framework, that we described in sec:back:cnn-based-detectors:single-stage-detectors. In a nutshell, unlike previous detectors based on a sliding-window classifier or region proposals, yolo addresses the detection problem by exploiting the one-look paradigm, achieving higher inference speed, and making it more suitable for working in limited computation capabilities scenarios.
